
[rgb]0,0, 0To verify that [rgb]0.,0,0our feature encoder can produce task-specific representations that facilitate the other encoder-agnostic methods, we also conduct related experiments with [rgb]0,0,0I3D as presented in Table REF .
It is noticeable that all results of encoder-agnostic methods are boosted after using our MIST fine-tuned features,
[rgb]0.,0.,0showing a reduction in the domain gap.
For example, AR-Net [1]} increases from 85.38% to 92.27% on the UCF-Crime dataset and achieves an improvement of 6.89% on the ShanghaiTech dataset. Therefore, our MIST can produce a more powerful [rgb]0.,0,0task-specific feature encoder that can be utilized in other approaches. We visualize the feature space of the pretrained I3D vanilla feature encoder and the MIST-fine-tuned encoder via t-SNE[2]} in Figure REF , which also indicates the refinement of feature representations.
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